The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.
The expansion of the Internet and the World Wide Web (“web”) has resulted in the rapid proliferation of web sites and web pages accessible to users. It is estimated that as of December 2007 close to 70 million active web sites existed on the Internet. (December 2007 Web Survey, http://news.netcraft.com/archives/2007/12/index.html). Not surprisingly, then, users can find a web site on almost any conceivable topic of interest.
While the diversity of information available on the Internet has exploded, techniques for helping users visually discern the relevancy and popularity of web pages have not kept pace. Specifically, users have difficulty telling, from viewing a web page, how popular that web page is or what user activity associated with the web page has taken place. Often, a web page viewed by the hundredth visitor appears the same as the web page did to the first visitor. The hundredth visitor has little idea, from viewing the web page, who the previous visitors were and how those previous visitors interacted with the web page. Information about how previous visitors interacted with the web page is valuable to current visitors, as such information provides an indication of the popularity and relevancy of the content on the web page.
Interacting with a web page is one example of the many possible user activities that may be conducted by users. In general, user activities may be offline or online. Typically, an online user activity involves a user requesting an online resource or a service from one or more online services capable of providing the requested resource or service. Resources may include documents, images, video, and the like. Services may be as varied as providing web pages, e-mail services, instant messaging services, etc.
An offline user activity includes any physical user activity conducted by a user and that may be represented by data that describes the physical activity. Such data may be collected, for example, by electronic physical sensors that collect information about the physical activity as it occurs. For example, consider the scenario of a user jogging along her favorite jogging path. If the jogger carries a global positioning satellite (GPS) device with her as she jogs then the device can collect global positioning information that describes the location and path of her jog. This information may be reported by the GPS device to an online service by using, for example, a wireless communications network or a physical cable that connects the device to a networked device that communicates the information over a data network to the online service.
Alternatively, a user may provide information that describes an offline physical activity directly to an online service. Continuing the jogging example, the jogger may provide information that describes her jog to an online service after she has completed the jog. In both cases, whether information that describes an offline physical activity is collected by electronic sensors or directly from a user, the information describes the offline physical activity itself and not the online activity of reporting the information to an online service.
Some information resources such as web pages provide limited visual representations of user activity. For example, a hit counter may be displayed on a web page to visually represent the number of times that the web page has been viewed by visitors to the web page. However, hit counters are limited in their ability to visually represent user activity. A hit counter does not visually convey some significant information that would help the current visitor discern the relevancy and popularity of the web page such as, for example, the identity of the previous visitors to the web page.
A tag cloud is a more recent example of a limited technique for visual representation of user activity. A tag is user-created keyword or category label that is associated with a web resource such as a document or an image. Tag clouds provide an aggregate display of tag-usage statistics. Specifically, tag clouds provide a visual cue of the most popular tags and the relative popularity of those tags. For example, a user who uploads photos from a recent birthday party to a photo sharing website may tag the photos with the keyword “birthday.” Another user uploading photos from a recent trip to San Francisco may tag the photos with the category label “San Francisco.” The photo-sharing web site may display the tags associated with uploaded photos in a tag cloud. In such a tag cloud, each tag has a corresponding weight. For each tag in a tag cloud, a visual cue is provided to give an indication of the popularity of the tag. For example, if there are more photos tagged with “birthday” than with “San Francisco,” then the “birthday” tag might be displayed in a larger font size relative to the “San Francisco” tag.
However, tag clouds are limited in their ability to represent user activities other than tagging web resources. Further, visual representations of tags are limited to manipulations of the tag text, such as changing font size.
In addition to web pages, there are numerous other digital information resources such as databases and electronic documents for which there is a need to be able to efficiently visually discern the user activity associated with the information resource. Current techniques for determining user activity associated with an information resource require inspection of information resource itself and perhaps other information resources containing metadata that describe user activity associated with the information resource of interest. Further, there is no known universal technique for visually representing user activity associated with an information resource that can be applied to both web pages and other types of information resources such as databases and documents.